Nabil Anwer: h-index, Total Citations, and Citation Map
Nabil Anwer's h-index is 34 (100 i10-index, 8,796+ total citations across 292+ publications) according to Google Scholar as of May 2026. Nabil Anwer is affiliated with Professor, LURPA, Univ. Paris-Saclay, ENS Paris-Saclay.
Nabil Anwer is a researcher affiliated with Professor, LURPA, Univ. Paris-Saclay, ENS Paris-Saclay, specializing in Tolerancing, Tolerance_Analysis, geometrical metrology. Their work has been cited 8,796 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Nabil Anwer's Citation Metrics
Bibliometric impact based on 292 indexed publications. Of these, 18 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 34
- i10-Index
- 100
- Total Citations
- 8,796
- Citing Countries
- 77
As of May 2026.
Nabil Anwer has an h-index of 34 and 8,796 total citations across 292 publications, with research cited by institutions in 77 countries.
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We've mapped 5,000 of 8,796 citations for Nabil Anwer
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Global Impact Map
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Top Cited Works
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Enabling technologies and tools for digital twin
20192,052
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
949 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for Digital Twins in engineering, subsequently expanding the field through enabling technologies and AI-driven predictive models for additive manufacturing.
The researcher established a foundational framework for assembly-based Design for Additive Manufacturing, subsequently advancing it through rigorous deviation modeling and shape transformation methodologies.
The researcher developed a framework for managing uncertainty in tolerancing from conceptual design to final product, extending this methodology to assess assemblability in metal additive manufacturing processes.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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